The zip file contains a file geodatabase of the trace network, layer (.lyrx) file, and a user guide. Please read the user guide to obtain necessary background information, data layers, and the version of ArcGIS Pro needed to view and perform the trace network. You'll have to repoint the broken source to unzipped file geodatabase.The trace network represents the LA County's hybrid hydrological network. Surface flow (flow accumulation > 5,000) was created using derive continuous flow method with LARIAC4 (2016) DEM and integrated with the County's storm drain network.Download zip file:https://pwsmpm.blob.core.windows.net/mapping/Trace_Network/Los_Angeles_County_Hybrid_Hydrological_Network.zipIf you have any questions, please contact us at mapping@dpw.lacounty.govMapping and GIS Services SectionSurvey/Mapping and Property Management Los Angeles County Public Works

https://github.com/EldhosePaul-2023/meshlab

Goal of the project is to measure and compare the performance of several forwarding techniques on different devices.

Compare the following four forwarding techniques/implementions regarding their performance:

eBPF (TC or XDP)

IP forwarding

IP forwarding with software offloading

IP forwarding with hardware offloading

In modern network measurement research, there exists a clear and demonstrable need for open sharing of large-scale network traffic datasets between organizations. Beyond network measurement, many security-related fields, such as those focused on detecting new exploits or worm outbreaks, stand to benefit given the ability to easily correlate information between several different sources. Currently, the primary factor limiting such sharing is the risk of disclosing private information. While prior anonymization work has focused on traffic content, analysis based on statistical behavior patterns within network traffic has, so far, been under-explored. This thesis proposes a new behavior-based approach towards network trace source-anonymization, motivated by the concept of anonymity-by-crowds, and conditioned on the statistical similarity in host behavior. Novel time-series models for network traffic and kernel metrics for similarity are derived, and the problem is framed such that anonymity and statistics-preservation are congruent objectives in an unsupervised-learning problem. Source-anonymity is connected directly to the group size and homogeneity under this approach, and metrics for these properties are derived. Optimal segmentation of the population into anonymized groups is approximated with a graph-partitioning problem where maximization of this anonymity metric is an intrinsic property of the solution. Algorithms that guarantee a minimum anonymity-set size are presented, as well as novel techniques for behavior visualization and compression. Empirical evaluations on a range of network traffic datasets show significant advantages in both accuracy and runtime over similar solutions.

The dataset is collected from 15 participants wearing 5 Shimmer wearable sensor nodes on the locations listed in Table 1. The participants performed a series of 16 activities (7 basic and 9 postural transitions), listed in Table 2.

The captured signals are the following:

3-axis accelerometer

3-axis gyroscope

3-axis magnetometer

The sampling rate of the devices is set to 51.2 Hz.

DATASET FILES

The dataset contains the following files:

partX/partXdev1.csv

partX/partXdev2.csv

partX/partXdev3.csv

partX/partXdev4.csv

partX/partXdev5.csv

Where X corresponds to the participant ID, and numbers 1-5 to the device IDs indicated in Table 1.

Each .csv file has the following format:

Column1: Device ID

Column2: accelerometer x

Column3: accelerometer y

Column4: accelerometer z

Column5: gyroscope x

Column6: gyroscope y

Column7: gyroscope z

Column8: magnetometer x

Column9: magnetometer y

Column10: magnetometer z

Column11: Timestamp

Column12: Activity Label

Table 1: LOCATIONS

Left Wrist

Right Wrist

Torso

Right Thigh

Left Ankle

Table 2: ACTIVITY LABELS

(Arrows (->) indicate transitions between activities)

stand

sit

sit and talk

walk

walk and talk

climb stairs (up/down)

climb stairs (up/down) and talk

stand -> sit

sit -> stand

stand -> sit and talk

sit and talk -> stand

stand -> walk

walk -> stand

stand -> climb stairs (up/down), stand -> climb stairs (up/down) and talk

climb stairs (up/down) -> walk

climb stairs (up/down) and talk -> walk and talk

Description This trace contains thirty days worth of all wide-area TCP connections between the Lawrence Berkeley Laboratory (LBL) and the rest of the world. Format The reduced trace was generated by tcp-reduce, and has the format explained in that script s documentation . Briefly, the trace is an ASCII file with one line per connection, with the following columns: timestamp duration protocol bytes sent by originator of the connection, or ? if not available bytes sent by responder to the connection, or ? if not available local host - the (renumbered) LBL host that participated in the connection remote host - the remote (non-LBL) host that participated in the connection. Remote hosts have not been renumbered, to allow for geographic analysis of the data. Please do not attempt any further traffic analysis regarding the remote hosts. state that the connection ended in. The two most important states are SF, indicating normal SYN/FIN completion, and REJ, indicating a rejected connection (in

This data set contains packet captures (PCAPs) of a 5G campus network.The corresponding paper can be found at 5G Campus Networks: A First Measurement Study Acknowledgement:Funded by the German Research Foundation (DFG

In this workflow, your job is to run an isolation trace using the Network Trace widget in the Utility Isolation Trace app.

For this dataset, we took the RETRO Requirement Specification (version 1.0, written to document the features in our original RETRO tool) and used RETRO.NET to trace it to the code used to implement RETRO.NET (copyright Jody Larsen). There are 66 requirements (functional requirements only) that have been extracted from the document. RETRO.NET expects all source elements to be in a folder and all target elements to be in a folder. Therefore, in our dataset, each requirement has been stored in its own file with the identifier as the file name and the file containing its text (RETRONET Requirements folder). The original requirements specification as well as the document subset have been provided in the dataset (as .docx and as data.txt, respectively) along with the python script (parser.py, copyright to Jared Payne) that was used to parse the text only version of the document subset. There are 118 code files in the dataset, primarily C# files. Each code file is listed in the code directory (RETRONET Trunk folder). The answer set has been provided in xml format (result.xml) as well as in traditional answer set format of our research group (results.txt).

The dataset contains all data used during the evaluation of trace meaning preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

The following list contains trace datasets used for evaluation. Each attack was chosen to have not only a different meaning but also different statistical properties.

• dos_http_flood — the capture of GET and POST requests sent to one server by one attacker (HTTP~traffic);
• ftp_bruteforce — short and unsuccessful attempt to guess a user’s password for FTP service (FTP traffic);
• ponyloader_botnet — Pony Loader botnet used for stealing of credentials from 3 target devices reporting to single IP with a large number of intermediate addresses (DNS and HTTP traffic);
• scan — the capture of nmap tool that scans given subnet using ICMP echo and TCP SYN requests (consist of ARP, ICMP, and TCP traffic);
• wannacry_ransomware — the capture of Wanacry ransomware that spreads in a domain with three workstations, a domain controller, and a file-sharing server (SMB and SMBv2 traffic).

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

• Traces variants (traces.zip)
  • ./traces-original/ — trace PCAP files and crawled details in YAML format;
  • ./traces-normalized — normalized PCAP files and details in YAML format;
  • ./traces-adjusted — adjusted PCAP files using various timestamp generation settings, combination configuration in YAML format, and lables provided by ID2T in XML format.
• Extracted alerts (alerts.zip)
  • ./alerts-original/ — extracted Suricata alerts, Suricata log, and full Suricata output for all original trace files;
  • ./alerts-normalized/ — extracted Suricata alerts, Suricata log, and full Suricata output for all normalized trace files;
  • ./alerts-adjusted/ — extracted Suricata alerts, Suricata log, and full Suricata output for all adjusted trace files.
• Evaluation results
  • *.csv files in the root directory — data contains extracted alert signatures and their count per each trace variant.

The nonzero density and average degree for each set and networks constructed by various methods in Control-Healthy and Control-EBA group.

One day of Netflow version 5 collected in flow tools format at an academic department. Collection includes traffic between all switches within the department and the egress switch to the college, university, and Internet. Departmental IP addresses in the flows are anonymized via prefix preserving anonymization.

TRAGNET United States Trace Gas Network

    The United States Trace Gas Network (TRAGNET) is meant to
    accomplish two goals, including documenting contemporary fluxes
    of CO2, CH4 and N2O between regionally important ecosystems and
    the atmosphere, and determining the factors controlling these
    fluxes and improve our ability to predict future fluxes in
    response to ecosystem and climate change. The research for this
    project is funded by the National Science Foundation.

    The atmospheric concentrations of greenhouse gases such as
    carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are
    increasing substantially. These increases are expected to result
    in global warming and changes in precipitation patterns, and may
    directly affect terrestrial ecosystems. Our understanding of the
    contemporary fluxes of these gases between the land and
    atmosphere is incomplete. There are large regions of the earth
    for which we have very little information on trace gas
    fluxes. Furthermore, for no region do we fully understand how
    global change, including land-use change, will affect gas
    fluxes.

    Data URL: "http://nrel.colostate.edu/projects/tragnet/tragnetSites.html"
    Information taken from "http://nrel.colostate.edu/projects/tragnet/"

This version 25.01 dataset collection consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN1028/06/2015, TRN1537/06/2018, TRN5488/11/2021 and prj_1604) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases.

The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet within 10 metres of ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks.

Robert B. Duckrow, Enea Ceolini, Hitten P. Zaveri, Cornell Brooks, & Arko Ghosh (2021) iScience

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 1 of the DetailedDescription document in the Supplementary Materials repository).
The current repository pertains to Scenario E. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.

OC48 packet header trace from a peering point in a large ISP's network on April 24, 2003.

No description is available. Visit https://dataone.org/datasets/ess-dive-508c97dc2f5a1a1-20230407T161249980850 for complete metadata about this dataset.

10 km wide corridor on either side of the gas transport network. This corridor was calculated from data “simplified route of the GRTgaz network precise to approximately 250 m » and “simplified trace of the TEREGA network precise to approximately 250 m present on the ODRÉ. This dataset reflects the presence of a gas transmission network within a 10 km radius at any point within the corridor. Attention ! This information does not make it possible to precisely identify the presence or absence of networks or pipelines in a given geographical area. Under no circumstances may you use them for road work. In this context, only information obtained via the teleservice (one-stop shop) http://www.reseaux-et-canalisations.ineris.fr is authentic.

The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

• Traces variants (traces-normalized.zip, traces-adjusted.zip)
  • ./traces-normalized/ — normalized PCAP files and details in YAML format;
  • ./traces-adjusted/ — configuration files for traces combination in YAML format.
• Computed statistics (statistics.zip)
  • ./statistics-background/ — background traffic statistics computed by ID2T;
  • ./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);
  • ./statistics-difference/ — computed mean and median differences of background and combined traffic traces.
• Evaluation results
  • statistics-difference.ipynb — file containing visualization of statistics differences.

The U.S. Geological Survey (USGS) National Water Quality Network - Rivers and Streams (NWQN) comprises 117 surface-water monitoring sites designed to track ambient water-quality conditions across the nation. This dataset includes field quality-control results (field blank and field replicate concentrations), along with the water-quality result of each associated surface-water sample, of water samples collected from October 2012 through September 2017 at NWQN sites. This dataset includes 2 tables and 6 files of plots of the data. Tables are in Comma Separated Value, CSV, format and plotfiles are in Portable Document Format, PDF, format. The plotfiles are intended to provide a succinct view of the data. Table1.NWQNFieldBlanksC3.csv Table2.NWQNFieldReplicatesC3.csv PlotFile 1. Time-series plots showing concentrations of nutrients, carbon, UV absorbance, and suspended sediments in surface-water samples and field blanks in the National Water Quality Network, water years 2013–17. Plot ...